| 5-K25-EB035166-02 |
New Tools for Enhancing Cerebral Angiography: From Planning to Navigation |
Nazim Haouchine |
Brigham And Women'S Hospital |
| 5-R01-EB031032-04 |
Non-invasive automated wound analysis via deep learning neural networks |
Kyle Quinn |
University of Arkansas at Fayetteville |
| 1-R01-EB036037-01A1 |
Optimization and Validation of an AI Model that Screens for Arteriovenous Fistula Stenosis in Dialysis Patients using Sound Files from a Digital Stethoscope |
Bobak Mosadegh |
Weill Medical Coll of Cornell Univ |
| 5-R01-EB035394-02 |
Optimizing Mobile Photon-Counting CT Image Quality via Deep Learning for Neuro Intensive Care Unit |
Dufan Wu |
Massachusetts General Hospital |
| 5-R01-EB022573-08 |
Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth |
Yong Fan |
University of Pennsylvania |
| 1-R21-EB034428-01A1 |
Predicting recovery after TBI: Development and comparison of MR-supplemented models using non-parametric and machine learning multimodal fusion |
Martin Monti |
University of California Los Angeles |
| 5-R01-EB030582-04 |
Quantification of Liver Fibrosis with MRI and Deep Learning |
Lili He |
Cincinnati Childrens Hosp Med Ctr |
| 1-R01-EB036013-01A1 |
Resolution Enhancement and Contrast Harmonization for MR Neuroimaging |
Jerry Prince |
Johns Hopkins University |
| 5-R01-EB032896-04 |
SCH: Leverage clinical knowledge to augment deep learning analysis of breast images |
Shandong Wu |
University of Pittsburgh at Pittsburgh |
| 7-R01-EB034116-03 |
SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate |
Heng Huang |
Univ of Maryland, College Park |
